Bibliographic record
Abstract
1. The Letter of Credit and its Legal Framework. The Nature of Letters of Credit. The Legal Framework of Letters of Credit. Summary. 2. The Fraud Rule: An Overview. Meaning and Rationale. Historical Development. The Fraud Rule in the United States. The Fraud Rule in Other Jurisdictions. The Fraud Rule in ICC Rules. The Fraud Rule under the UNCITRAL Convention. Summary. 3. The Standard of Fraud. The Position in the US. The Position in the UK. The Position in Canada. The Position in Australia. The Position under the UNCITRAL Convention. Summary. 4. The Locus of Fraud. The Controversy. Case Studies. The Solution. Commentary. 5. Identity of the Fraudulent Party. Statutory Provisions. Applicant Fraud. Third Party Fraud: The Case of United City Merchants. Summary. 6. Presenters Immune from the Fraud Rule. Preliminary Observations. Likely Presenters. Conclusion. 7. The Fraud Rule in the People's Republic of China. The Legal System of the PRC. The Fraud Rule in the PRC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".